Pan-Cancer Detection and Typing by Mining Patterns in Large Genome-Wide Cell-Free DNA Sequencing Datasets
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F22%3A10445198" target="_blank" >RIV/00064165:_____/22:10445198 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11110/22:10445198
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=VXk7f0Zune" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=VXk7f0Zune</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1093/clinchem/hvac095" target="_blank" >10.1093/clinchem/hvac095</a>
Alternative languages
Result language
angličtina
Original language name
Pan-Cancer Detection and Typing by Mining Patterns in Large Genome-Wide Cell-Free DNA Sequencing Datasets
Original language description
Background: Cell-free DNA (cfDNA) analysis holds great promise for non-invasive cancer screening, diagnosis, and monitoring. We hypothesized that mining the patterns of cfDNA shallow whole-genome sequencing datasets from patients with cancer could improve cancer detection. Methods: By applying unsupervised clustering and supervised machine learning on large cfDNA shallow whole-genome sequencing datasets from healthy individuals (n = 367) and patients with different hematological (n = 238) and solid malignancies (n = 320), we identified cfDNA signatures that enabled cancer detection and typing. Results: Unsupervised clustering revealed cancer type-specific sub-grouping. Classification using a supervised machine learning model yielded accuracies of 96% and 65% in discriminating hematological and solid malignancies from healthy controls, respectively. The accuracy of disease type prediction was 85% and 70% for the hematological and solid cancers, respectively. The potential utility of managing a specific cancer was demonstrated by classifying benign from invasive and borderline adnexal masses with an area under the curve of 0.87 and 0.74, respectively. Conclusions: This approach provides a generic analytical strategy for non-invasive pan-cancer detection and cancer type prediction.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30204 - Oncology
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Clinical Chemistry
ISSN
0009-9147
e-ISSN
1530-8561
Volume of the periodical
68
Issue of the periodical within the volume
9
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
1164-1176
UT code for WoS article
000818628600001
EID of the result in the Scopus database
2-s2.0-85137136802